Cloud-Native Revenue Intelligence: Streaming Analytics for Big Data Operations

Abstract

The exponential growth of enterprise data has intensified the need for real-time intelligence systems that can support revenue optimization and operational resilience. This study investigates the integration of cloud-native architectures and streaming analytics within big data operations to evaluate their impact on system performance and business outcomes. Using a Kubernetes-based deployment with Apache Kafka, Flink, and Spark Structured Streaming, experiments were conducted on simulated and benchmark datasets ranging from 100 GB to 5 TB and event rates up to 50,000 per second. Results show that multi-cloud deployments significantly outperformed hybrid models, achieving higher throughput and lower latency while maintaining robust fault tolerance. Machine learning models demonstrated strong predictive capabilities, with LSTM achieving forecasting accuracy of 94.1% and XGBoost excelling in anomaly detection. Business impact analysis revealed substantial improvements, including a 295% increase in revenue leakage reduction, enhanced churn prediction accuracy, and improved fraud detection rates. Regression analyses further confirmed that infrastructure variables such as cluster size, latency, and replication factor strongly influence predictive and operational outcomes. Overall, the study establishes cloud-native revenue intelligence as a strategic imperative for enterprises seeking continuous intelligence and competitive advantage in data-driven markets.